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Forecasting Euro-area recessions using time-varying binary response models for financial

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  • Bellégo, C.
  • Ferrara, L.

Abstract

Recent macroeconomic evolutions during the years 2008 and 2009 have pointed out the impact of financial markets on economic activity. In this paper, we propose to evaluate the ability of a set of financial variables to forecast recessions in the euro area by using a non-linear binary response model associated with information combination. Especially, we focus on a time-varying probit model whose parameters evolve according to a Markov chain. For various forecast horizons, we provide a readable and leading signal of recession by combining information according to two combining schemes over the sample 1970-2006. First we average recession probabilities and second we linearly combine variables through a dynamic factor model in order to estimate an innovative factor-augmented probit model. Out-of-sample results over the period 2007-2008 show that financial variables would have been helpful in predicting a recession signal as September 2007, that is around six months before the effective start of the 2008-2009 recession in the euro area.

Suggested Citation

  • Bellégo, C. & Ferrara, L., 2009. "Forecasting Euro-area recessions using time-varying binary response models for financial," Working papers 259, Banque de France.
  • Handle: RePEc:bfr:banfra:259
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    References listed on IDEAS

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    Cited by:

    1. Faia, Ester, 2010. "Credit risk transfers and the macroeconomy," CFS Working Paper Series 2010/26, Center for Financial Studies (CFS).
    2. Adrian Pagan & Don Harding, 2011. "Econometric Analysis and Prediction of Recurrent Events," NCER Working Paper Series 75, National Centre for Econometric Research.
    3. Christophe Bellégo & Laurent Ferrara, 2010. "A factor-augmented probit model for business cycle analysis," EconomiX Working Papers 2010-14, University of Paris Nanterre, EconomiX.
    4. Berardi, Nicoletta, 2009. "The Remains of Informality in the Formal Sector: Social Networks and Wages in Senegal's Labor Market," TSE Working Papers 09-129, Toulouse School of Economics (TSE).
    5. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2014. "Dynamic factor models: A review of the literature," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, pages 73-107.
    6. Adrian Pagan & Don Harding, 2011. "Econometric Analysis and Prediction of Recurrent Events," NCER Working Paper Series 75, National Centre for Econometric Research.
    7. Laurent Ferrara & Clément Marsilli, 2013. "Financial variables as leading indicators of GDP growth: Evidence from a MIDAS approach during the Great Recession," Applied Economics Letters, Taylor & Francis Journals, vol. 20(3), pages 233-237, February.
    8. Vincent Vicard & Emmanuelle Lavallée, 2013. "National borders matter...where one draws the lines too," Post-Print hal-01548193, HAL.
    9. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2014. "Dynamic factor models: A review of the literature," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, pages 73-107.

    More about this item

    Keywords

    Macroeconomic forecasting; Business cycles; Turning points; Financial markets; Non-linear time series; Combining forecasts.;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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